Probability Theory II: Assignment 3 Problems for Spring 2003, Assignments of Probability and Statistics

Problems for assignment 3 of the probability theory ii course offered in spring 2003. The problems involve independent and identically distributed random variables, convergence in probability, and orthogonal random variables. Students are required to turn in the first three problems, while the remaining problems are optional. Hints are provided for some problems.

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Pre 2010

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STA 6467: Probability Theory II
Spring 2003
Problems for Assignment 3
Turn in the first three problems. The remaining problems are optional problems that you may wish to
try, but they will not be collected. Other optional problems from Section 22 of Billingsley are listed on the
course web page.
1. Let {Xn}be independent and identically distributed with mean 0and variance 1. Then we have for
every x:
Pmax
1knSkx2PSnx2n
Hint: Let τbe the first time kat which Skxand show that
PSnx2n
n
X
k=1
Pτ=khSnSk 2ni.
2. For any sequence of random variables {Xn}and Sn=Pn
k=1 Xk, it is easy to show that
Sn
n
a.s.
0implies max1kn|Sk|
n
a.s.
0
Suppose that X1, X2, . . . are independent. Show in this case that
Sn
n
P
0implies max1kn|Sk|
n
P
0.
Hint: Use Etemadi’s inequality.
3. Let Xn0be independent for n1. The following are equivalent:
(i) P
n=1 Xn<a.s..
(ii) P
n=1P(Xn>1) + E[XnI[Xn1]]<.
(iii) P
n=1 EXn/(1 + Xn)<.
4. Let {Xn, n 1}be independent. Then for any α,
Pmax
1knSk2α×min
1jnP
SnSj
αP|Sn| α.
Hint: Consider sets of the form Sj<2α, 1jk1, Sk2α, |SnSk|< α.
5. For arbitrary {Xn}, if
X
n
E(|Xn|)<,
then PnXnconverges absolutely a.s.
pf2

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STA 6467: Probability Theory II

Spring 2003

Problems for Assignment 3

Turn in the first three problems. The remaining problems are optional problems that you may wish to

try, but they will not be collected. Other optional problems from Section 22 of Billingsley are listed on the

course web page.

  1. Let {Xn} be independent and identically distributed with mean 0 and variance 1. Then we have for

every x:

P

max 1 ≤k≤n

Sk ≥ x

≤ 2 P

Sn ≥ x −

2 n

Hint: Let τ be the first time k at which Sk ≥ x and show that

P

Sn ≥ x −

2 n

∑^ n

k=

P

[

τ = k

]

[

Sn − Sk ≥ −

2 n

])

  1. For any sequence of random variables {Xn} and Sn =

∑n k=1 Xk, it is easy to show that

Sn

n

a.s. −→ 0 implies

max 1 ≤k≤n|Sk|

n

a.s. −→ 0

Suppose that X 1 , X 2 ,... are independent. Show in this case that

Sn

n

P → 0 implies

max 1 ≤k≤n|Sk|

n

P → 0.

Hint: Use Etemadi’s inequality.

  1. Let Xn ≥ 0 be independent for n ≥ 1. The following are equivalent:

(i)

n=1 Xn^ <^ ∞^ a.s..

(ii)

n=

P (Xn > 1) + E[XnI[Xn≤1]]

(iii)

n=1 E

[

Xn/(1 + Xn)

]

  1. Let {Xn, n ≥ 1 } be independent. Then for any α,

P

max 1 ≤k≤n

Sk ≥ 2 α

× min 1 ≤j≤n

P

∣Sn − Sj

∣ (^) ≤ α

≤ P

|Sn| ≥ α

Hint: Consider sets of the form

[

Sj < 2 α, 1 ≤ j ≤ k − 1 , Sk ≥ 2 α, |Sn − Sk| < α

]

  1. For arbitrary {Xn}, if ∑

n

E(|Xn|) < ∞,

then

n Xn^ converges absolutely a.s.

  1. Let {Xn, n ≥ 2 } be a sequence of independent random variables with the following discrete distribu-

tions:

P [Xn = n^2 ] = P [Xn = −n^2 ] = n−^2

and

P [Xn = (−1)n] = 1 − 2 n−^2.

Prove whether or not n−^1

∑n k=2 Xk^ converges

(a) almost surely as n → ∞;

(b) in probability as n → ∞.

Characterize any limits which exist.

  1. Let {Xn, n ≥ 1 } be a sequence of orthogonal random variables (i.e., EX n^2 < ∞ for all n ≥ 1 and

E(XmXn) = 0 for all m 6 = n). Set Sn =

∑n j=1 Xj^ ,^ n^ ≥^1. Prove that if^

n=1 EX

2 n <^ ∞, then there exists a random variable S such that

Sn

L^2 −→ S.

  1. Prove for every p ∈ (0, 1) that there exists a sequence of random variables {Xn, n ≥ 1 } such that

P

{ (^) ∑n

j=

Xj converges

= p.

Why doesn’t this problem contradict the Kolmogorov 0-1 law?